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. 2023 Jan 27;11(5):2321–2335. doi: 10.1002/fsn3.3236

LC/MS analysis of mushrooms provided new insights into dietary management of diabetes mellitus in rats

Abdelaziz Hussein 1,2,3, Abdallah Ghonimy 4,5,6, Hailong Jiang 1,2,✉, Guixin Qin 1,2, Saeed El‐Ashram 7,8, Saddam Hussein 1, Ibrahim Abd El‐Razek 9, Tarek El‐Afifi 3, Mohammed Hamdy Farouk 10,✉
PMCID: PMC10171545  PMID: 37181306

Abstract

Mushrooms possess antihyperglycemic effect on diabetic individuals due to their nonfibrous and fibrous bioactive compounds. This study aimed to reveal the effect of different types of mushrooms on plasma glucose level and gut microbiota composition in diabetic individuals. The effects of five different mushroom species (Ganoderma lucidum, GLM; Pleurotus ostreatus, POM; Pleurotus citrinopileatus, PCM; Lentinus edodes, LEM; or Hypsizigus marmoreus, HMM) on alloxan‐induced diabetic rats were investigated in this study. The results indicated that LEM and HMM treatments showed lower plasma glucose levels. For the microbiota composition, ACE, Chao1, Shannon, and Simpson were significantly affected by PCM and LEM treatments (p < .05), while ACE, Shannon, and Simpson indexes were affected by HMM treatment (p < .01). Simpson index was affected in positive control (C+) and POM groups. All these four indices were lower in GLM treatment (p < .05). Dietary supplementation of mushrooms reduced plasma glucose level directly through mushrooms' bioactive compounds (agmatine, sphingosine, pyridoxine, linolenic, and alanine) and indirectly through stachyose (oligosaccharide) and gut microbiota modulation. In conclusion, LEM and HMM can be used as food additives to improve plasma glucose level and gut microbiome composition in diabetic individuals.

Keywords: 16S rRNA, alloxan, diabetes, dietary fiber, microbiota, mushroom

The effect of mushroom's bioactive compounds on plasma glucose level. Agmatine increases insulin secretion and glucose uptake in muscle. Sphingosine activates omega‐3 fatty acid receptor (GPR120) mediating potent insulin‐sensitizing effects. Pyridoxine decreases insulin resistance. Stachyose regulates the intestinal microflora balance.

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1. INTRODUCTION

Mushrooms possess a necessary dietary ingredient due to their low‐calorie value, different bioactive compounds, and rich fiber content. These compounds include vitamins (such as riboflavin and niacin), minerals (such as iron and phosphorus) (Alkin et al., 2021), and fibers (Lu et al., 2020). Moreover, mushrooms differ in their content of phenols (Alkin et al., 2021) and antioxidants (ergothioneine and glutathione) (Beelman et al., 2019). Thus, each mushroom type possesses different content of bioactive compounds, fiber contents, or both (Alkin et al., 2021). Gut microbiota degrades the dietary fibers as a nondigestible carbon sources and change the microbiota composition (Tang et al., 2017). Unfavorable modulations of gut microbiota are associated with multiple chronic diseases including diabetes mellitus (Tang et al., 2017). In particular, mushrooms possess enriched fibers, such as polysaccharides and heteroglucans (Li et al., 2021), these decrease a pathogen proliferation by inducing the growth of probiotic bacteria in gut (Kumari, 2020). For instance, the species of Ganoderm lucidum is a medicinal mushroom which contains various bioactive compounds that operates as antimicrobial agents (Cor et al., 2018). Fruiting bodies and mycelia of G. lucidum contain polysaccharides, such as glycoproteins, (1 → 3), (1 → 6)‐a/β‐glucans, and water‐soluble heteropolysaccharides (Martin & Jiang, 2010). Those polysaccharides have antihypoglycemic effect (Chassaing et al., 2017; Xu et al., 2017). G. lucidum increases anti‐inflammatory bacteria (Enterococcus and Dehalobacterium) in mice diabetic individuals (Chen, Liu, et al., 2020; Chen, Xiao, et al., 2020) and decreases the abundance of harmful bacteria, such as Aerococcus, Corynebactrium, Ruminococcus, and Proteus in type‐2 mice diabetic groups (Chen, Liu, et al., 2020; Chen, Xiao, et al., 2020). G. lucidum treatment on type‐2 diabetes, enhanced SCFA‐producing bacterial activity (Chen, Xiao, et al., 2020). In fact, oral administration of extracted polysaccharides from mushrooms (Pleurotus eryngii and Poria cocos) promote SCFA‐producing bacterial growth (Li et al., 2021), this improve energy metabolism through affecting the intestinal gluconeogenesis (IGN) and insulin sensitivity simulation (De Vadder et al., 2014). Since IGN releases glucose molecules which can be detected by the glucose sensor in the portal vein. Such signal is transmitted to the brain by the peripheral nervous system regulating glucose metabolism (Delaere et al., 2013). Polysaccharides inhibit digestive enzymes' activity based on the interaction with different sites at enzymes' structure. In addition, polysaccharide's viscoelasticity effect interferences with the enzymes and substrates flow resulting in lipid‐lowering effect (Xie et al., 2022).

Most mushroom‐related studies have investigated the effect of mushroom fibrous bioactive compounds on individual diabetics. While few studies have investigated the non‐fibrous bioactive compounds on diabetic individuals (Cor et al., 2018; Lu et al., 2020). Dubey et al. (2019) indicated that few bioactive compounds of edible mushrooms are identified. In a pilot study, mushroom untargeted molecules' composition was investigated, and analyzed; this revealed the existence of some of bioactive compounds in the tested mushrooms. The literature showed that agmatine has an antihyperglycemic effect through increasing glucose uptake in muscles by simulating insulin secretion (Chang et al., 2010; Malaisse et al., 1989; Naoki & Fujiwara, 2019; Nissim et al., 2006, 2014; Shepherd et al., 2012; Su et al., 2008, 2009). Whereas stachyose decreases the blood glucose level in alloxan‐induced diabetic rats (Zhang et al., 2004), in addition to its regulation effect on the intestinal microflora balance (Liu, Jia, et al., 2018; Liu, Wang, et al., 2018). Sphingosine (PHS) activates omega‐3 fatty acid receptor (GPR120) resulting in an insulin‐sensitizing effect (Rudd et al., 2020). Finally, pyridoxine decreases insulin resistance via scavenging the pathogenic reactive carbonyl species (Haus & Thyfault, 2018). There are epidemiological evidences supporting the non‐fibrous bioactive compounds safety use, eight mushrooms have been investigated for their effect on DM, that G. lucidum has showed the highest content of phenolic and flavonoids compounds (Wu & Xu, 2015). But this study did not analyze the other bioactive molecules. Generally, the non‐fibrous compounds are naturally existed in human foods, for example, agmatine in fermented foods (Galgano et al., 2012), stachyose (oligosaccharides) as a probiotic in human foods (Yang et al., 2018), sphingosine in dairy products (Possemiers et al., 2005), and pyridoxine is a vitamin B6 (Shaik Mohamed, 2001). This revealed a possible therapeutic potency of mushrooms on diabetic individuals.

Mushrooms are basic food components in Chinese table. Thus, revealing their nutritional value and their therapeutic potency are expected to increase the social awareness about dietary mushroom in addition to their therapeutic effect leading to the improving of public health.

To reveal the effect of those bioactive compounds in mushrooms on a diabetic individual, alloxan injection was used to induce the diabetes type II in rats by damaging pancreatic cells and initiating hyperglycemia (Inalegwu et al., 2021). These animals were subjected to different dietary mushrooms to reveal the effect of bioactive compounds sourced from mushrooms on blood glucose level and intestinal microbial composition as basic indicators for the possible effect.

We hypothesized that dietary mushrooms inclusion may decrease plasma glucose level and modulate microbiota composition directly via their bioactive molecules, and indirectly via fiber content and microflora modulation. This study aimed to reveal the effect of non‐fibrous bioactive compounds sourced from mushrooms on blood glucose level and intestinal microbial composition in diabetic rat individuals. These molecules may develop a food additive with an effective and specific health functionality considering food processing conditions and their effect on food quality.

2. MATERIALS AND METHODS

2.1. Experimental ethics and tested animals

This study was conducted under the ethical approval of Animal Welfare and Ethics Committee of the Key Laboratory of Animal Safety Production of Ministry of Education, PR China (no. KT2018011).

Diabetes mellitus is a metabolic disease characterized by hyperglycemia, occurring due to abnormal insulin action or insulin secretion. Alloxan induces diabetes type II by damaging pancreatic cells and initiating hyperglycemia (Inalegwu et al., 2021). Experimental animals Albino male rats (Rattus norvegicus) weighed 160–180 g with age 30 days were obtained from the animal's research center, Jilin Agricultural University, Changchun, China. Alloxan‐induced diabetic rats (AIDRs) were used as the diabetic model after acclimation period of 30 days. AIDRs were induced by intraperitoneal injection of Alloxan (150 mg/kg of body weight; Shanghai Sinyu Biotechnology Company) after an overnight fast. Three days after Alloxan injection, rats with a plasma glucose concentration of 11 mmol/L or above and symptoms of polyuria, polyphagia, and polydipsia were considered to have diabetes. The animals were distributed among treatments, seven animals per treatment (Table 1). All experimental animals were weighed weekly using a digital balance (Yeng Heng Electronic Scale Company) within the experimental period (4 weeks).

TABLE 1.

Experimental design

Groups Treatments Diet (%) No. of rats (n)
A C− 100% Commercial diet 7
B C+ 100% Commercial diet 5
C GLM 75% Commercial diet + 25% GLM 5
D POM 75% Commercial diet + 25% POM 5
E PCM 75% Commercial diet + 25% PCM 4
F LEM 75% Commercial diet + 25% LEM 5
G HMM 75% Commercial diet + 25% HMM 5
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Abbreviations: C−, negative control; C+, positive control; GLM, Ganoderma lucidum mushroom; HMM, Hypsizigus marmoreus mushroom; LEM, Lentinus edodes mushroom; PCM, Pleurotus citrinopileatus mushroom; POM, Pleurotus ostreatus mushroom.

2.2. Determination of plasma glucose

Animals were deprived of food and water overnight and a blood glucose meter and test strips (Hangzhou Econ Biotech Company) were used to measure the blood glucose levels.

2.3. Experimental diets and mushrooms' bioactive compounds

The effect of five types of mushrooms (GLM) Ganoderma lucidum mushroom (traditional Chinese medicinal mushroom), (POM) Pleurotus ostreatus mushroom, (PCM) Pleurotus citrinopileatus mushroom, (LEM) Lentinus edodes mushroom, and (HMM) Hypsizigus marmoreus mushrooms were tested in rats that were fed on a commercial diet (Beijing Keao Cooperative Feed Co.) (Table 2). Fresh mushrooms were obtained from the Base Centre of Jilin Agricultural University, Changchun, China. Mushroom fruiting bodies were dried under sunlight for 72 h, and crushed into powder using a laboratory mill. Mushroom powder was mixed with the commercial diet as a daily intake of 8.5 g per individual. The nominated mushrooms' bioactive compounds are shown in Table 3.

TABLE 2.

Chemical composition of experimental diets

Group CP (%) EE (%) CF (%) ADF (%) NDF (%) Ash (%) CHO (%)
C+ and C− 19 ± 0.1b 4 ± 1a 38 ± 0.2a 36 ± 0.03b 46 ± 0.2a 7.2 ± 0.05a 68 ± 1ab
GLM 19 ± 1b 2.4 ± 0.5a 41 ± 3a 42 ± 0.1a 49 ± 0.5a 4.6 ± 1.07a 72 ± 1a
POM 24 ± 1a 4.1 ± 0.7a 37 ± 0.2a 42.89 ± 0.3a 42 ± 3a 6.3 ± 0.02b 64 ± 0.4c
PCM 22 ± 3ab 3.8 ± 0.6a 38 ± 0.1a 37 ± 1b 44 ± 0.3ab 6.6 ± 0.05a 64 ± 0.3bc
LEM 21 ± 2ab 3.8 ± 0.1a 39 ± 1a 41 ± 0.5a 46 ± 0.6a 6.2 ± 0.06a 67 ± 2bc
HMM 23 ± 0.9ab 3.4 ± 0.08a 37 ± 0.03a 36 ± 2b 45 ± 2a 6.9 ± 0.02a 65 ± 0.9bc
p‐Value .073 .371 .295 .002 .141 .010 .013
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Note: Values represented means ± standard deviation. Different letters represent a significant difference in Tukey test at p < .05.

Abbreviations: ADF, acid detergent fiber; C−, negative control; C+, positive control; CF, crude fiber; CHO, carbohydrate; CP, crude protein; EE, ether extract; GLM, Ganoderma lucidum mushroom; HMM, Hypsizigus marmoreus mushroom; LEM, Lentinus edodes mushroom; NDF, neutral detergent fiber; PCM, Pleurotus citrinopileatus mushroom; POM, Pleurotus ostreatus mushroom.

TABLE 3.

Mushrooms' bioactive compounds (percentage of control)

Bioactive compound C GLM POM PCM LEM HMM
Agmatine 33 29 73 80 446 829
Stachyose 40 357 40 73 27 25
Sphingosine 31 94 18 23 68 621
Pyridoxine 70 247 32 112 25 21
Linolenic acid 68 104 82 99 145 143
Alanine 34 126 116 59 161 123
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Abbreviations: C, control diet; GLM, Ganoderma lucidum mushroom; HMM, Hypsizigus marmoreus mushroom; LEM, Lentinus edodes mushroom; PCM, Pleurotus citrinopileatus mushroom; POM, Pleurotus ostreatus mushroom.

2.4. Liquid chromatography‐mass spectrometry (LC–MS)

2.4.1. Sample preparation

Fifty milligrams of mushroom sample was mixed with 1 ml of the mixture (methanol:acetonitrile:water, 2:2:1). The sample was put into a multi‐tissue grinder (frequency 60 Hz, 4 min) for tissue fragmentation, and then ultrasonicated for 10 min and then it was kept in the refrigerator for 1 h. the sample was centrifuged at 4°C for 15 min at 10,000 g. Seven hundred microliters of supernatant was put in a vacuum freeze dryer until it evaporated. The solution was resuscitated with 500 μl acetonitrile water (1:1) for 30 s and ultrasonicated for 10 min. The centrifugation was performed at 4°C for 15 min at 10,000 g. A volume of 50 μl of supernatant was put into the injection bottle and detected by LC–MS.

2.4.2. Liquid phase conditions

We used a chromatographic column (Waters ACQUITY UPLC BEH Amide 1.7 μm, 2.1 mm × 100 mm).

2.4.3. Mobile phase

Phase A is ultrapure water containing 25 mM ammonium acetate and 25 mm ammonia, and phase B is acetonitrile. Current Speed 5 ml/min, column temperature 40°C, injection volume 2 μl.

2.4.4. Liquid phase elution gradient

A gradient elution high‐performance liquid chromatographic method is described in Table 4.

TABLE 4.

Liquid phase elution gradient

Time A% B% Flow rate (ml/min)
0 5 95 0.5
0.5 5 95 0.5
7 35 65 0.5
8 60 40 0.5
9 60 40 0.5
9.1 5 95 0.5
12 5 95 0.5
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2.4.5. Mass spectrometry conditions

Temperature of EFI ion source was 65°C. MS voltage was 5500 V (positive ion) and was 4500 V (negative ion). Declustering voltage DP was 6 0 V Ion source gas: gas 1 was 60 psi, gas 2 was 60 psi, and curtain gas (cur) was 30 psi.

2.5. DNA extraction, polymerase chain reaction amplification and high‐throughput sequencing

Next‐generation sequencing (NGS), including library preparations, was conducted at Genewiz, Inc. using an Illumina MiSeq (Illumina). DNA (30–50 ng) was extracted using TIANGEN DP336 genomic‐DNA extraction kits (TIANGEN Biotech [Beijing] Co. Ltd.) and quantified with a Qubit 2.0 Fluorometer (Invitrogen). To generate amplicons (400–450 bp), the MetaVx Library Preparation Kit (Genewiz) was used. For each 40 ng sample of DNA, V3, V4, and V5 hypervariable regions of prokaryotic 16S ribosomal RNA (rRNA) were selected for generating amplicons, following taxonomic analysis. Genewiz has designed a panel of proprietary primers aimed at relatively conserved regions bordering the V3, V4, and V5 hypervariable regions of bacteria and Archaea16S rDNA (if eukaryotic DNA was contaminated, only the V3 and V4 regions were amplified). V3 and V4 regions were amplified using forward primers containing the sequence CCTACGGRRBGCASCAGKVRVGAAT and reverse primers containing the sequence GGACTACNVGGGTWTCTAATCC, the V4 and V5 regions were amplified using forward primers containing the sequence GTGYCAGCMGCCGCGGTAA and reverse primers containing the sequence CTTGTGCGGKCCCCCGYCAATTC. First‐round polymerase chain reaction (PCR) products were used as templates for second‐round amplicon enrichment PCR. At the same time, indexed adapters were added to the ends of the 16S rDNA amplicons to generate indexed libraries for downstream NGS on the Illumina MiSeq according to Quast et al. (2013).

DNA libraries were validated using an Agilent 2100 Bioanalyzer (Agilent Technologies), quantified by Qubit 2.0 Fluorometer (Invitrogen), multiplexed and loaded onto the Illumina MiSeq as per manufacturer's instructions. NGS was performed using a 2 × 300 paired‐end (PE) configuration (Li, Hu, et al., 2017; Li, Wang, et al., 2017). Image analysis was conducted and base calling with the MiSeq Control Software (MCS) embedded in the MiSeq instrument (Yilmaz et al., 2014). The amplicon sequence data were deposited with the National Center for Biotechnology Information (Accession Nos. SRR2579284 and ERS2011824).

2.6. Sequence analysis

Quantitative Insights into Microbial Ecology (QIIME) data analysis software were used to analyze 16S rRNA data (Caporaso et al., 2010). Quality filtering was performed on raw sequences according to Bokulich et al. (2012), as well as on joined sequences. Any sequence that was not <200 bp, with no ambiguous bases and a mean quality score ≥20 was discarded. Forward and reverse reads were joined and assigned to samples based on barcode and truncated by cutting off the barcode and primer sequence. The sequences were compared with the reference database (Ribosomal Database Project [RDP] Gold database) using the UCHIME algorithm (Edgar et al., 2011) to detect chimeric sequences that were removed. Only effective sequences were used in the final analysis. Sequences were grouped into operational taxonomic units (OTUs) and pre‐clustered at 97% sequence identity using the clustering program VSEARCH version 1.9.6 (Rognes et al., 2016) against the SILVA 119 database. The RDP classifier was used to assign taxonomic categories to all the OTUs at a confidence threshold of 0.8, according to Crawford et al. (2009). The RDP classifier uses the SILVA 119 database, which predicts taxonomic categories to the species level. Sequences were rarefied prior to calculation of alpha and beta diversity statistics. Alpha diversity indices were calculated in QIIME from rarefied samples using the Shannon index for diversity and the Chao1 index for richness (Chao, 1984; Chao & Lee, 1992). Beta diversity was calculated using weighted and unweighted UniFrac and principal component analysis (Bamberger & Lowe, 1988). An unweighted Pair Group Method with Arithmetic mean (UPGMA) tree from beta diversity distance matrix was built.

2.7. Statistical analysis

Based on the beta diversity distance matrix and on environmental factor data, canonical correspondence analysis (CCA) between RFPs and BCC was integrated by the R‐language software application (Team, 2018). All data were analyzed by one‐way (mushroom type) analysis of variance (ANOVA) and were performed using SPSS‐software, version 11.5 (SPSS, Version 11.5.0; SPSS Inc.). Results were expressed as Mean ± SD. Tukey's contrasts were used to test the significance level for the effects of mushroom types, with p < .05 indicating significant difference.

3. RESULTS

3.1. Feed intake, plasma glucose level, and body weight

There were significant differences in feed intake between PCM and C−, and between POM and C+ (p < .05) (Table 5). All mushroom treatments showed a significant difference compared with C+ except GLM treatments in plasma glucose level, whereas the mushrooms treatments showed a gradually improved plasma glucose level till 45 days of experimentation (p < .05). LEM and HMM showed less significant difference compared with C− in plasma glucose level (p < .05).

TABLE 5.

Feed intake and plasma glucose level

Group Feed intake (g/day) Plasma glucose (mmol)
Before treatment After treatment
C− 37 ± 3bc 6 ± 0.4b 5 ± 0.3d
C+ 44 ± 2ab 26 ± 6a 23 ± 6a
GLM 43 ± 1ab 28 ± 5a 21 ± 4ab
POM 33 ± 12c 28 ± 4a 16 ± 3bc
PCM 47 ± 2a 26 ± 2a 15 ± 5c
LEM 40 ± 3abc 24 ± 8a 11 ± 5c
HMM 43 ± 2ab 25 ± 6a 12 ± 2c
p‐Value .005 .001 .001
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Note: Values represented means ± standard deviation. Different letters represent a significant difference in Tukey test at p < .05.

Abbreviations: C−, negative control; C+, positive control; GLM, Ganoderma lucidum mushroom; HMM, Hypsizigus marmoreus mushroom; LEM, Lentinus edodes mushroom; PCM, Pleurotus citrinopileatus mushroom; POM, Pleurotus ostreatus mushroom.

Mushroom treatments showed a significant difference compared with C− in body weight (p < .05) (Table 6). HMM treatment showed a significant difference compared with control (C− and C+) in liver weight (p < .05).

TABLE 6.

Body weight indices and plasma glucose levels in alloxan‐induced diabetic rats received normal or mushroom diets

Treatments Body weight (g)
First Second Third Final
C− 253 ± 5a 338 ± 22a 370 ± 21a 411 ± 31a
C+ 245 ± 4ab 267 ± 17b 283 ± 20b 234 ± 45b
GLM 248 ± 7ab 2790 ± 31b 296 ± 47b 246 ± 74b
POM 240 ± 6b 266 ± 21b 268 ± 33b 220 ± 43b
PCM 250 ± 5a 266 ± 20b 284 ± 20b 232 ± 48b
LEM 247 ± 8ab 268 ± 11b 276 ± 22b 221 ± 42b
HMM 244 ± 5ab 267 ± 12b 281 ± 23b 187 ± 39b
p‐Value .047 .001 .001 .001
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Note: Values represented means ± standard deviation. Different letters represent a significant difference in Duncan test at p < .05.

Abbreviations: C−, negative control; C+, positive control; GLM, Ganoderma lucidum mushroom; HMM, Hypsizigus marmoreus mushroom; LEM, Lentinus edodes mushroom; PCM, Pleurotus citrinopileatus mushroom; POM, Pleurotus ostreatus mushroom.

3.2. Untargeted molecules analysis by LC/MS

A wide variety of molecules have been identified in diet and mushroom samples, the molecules level was calculated as a percent of the quality control (QC) value, a percent over 100 was nominated as a possible effective molecule. Among them, few molecules have been discussed in this study based on the available literature regarding to the effect on diabetes (Table 7).

TABLE 7.

Untargeted molecule analysis by LC/MS

SN Molecule name Response (%)
Diet GLM POM PCM LEM HMM
1 (−)‐Riboflavin 175 150 6 136 72 69
2 1,3‐Diaminopropane 63 258 34 59 3 3
3 11‐Keto‐beta‐boswellic acid 51 3 30 87 374 360
4 4‐Methoxyphenylacetic acid 198 55 120 126 32 29
5 Acetylcholine 61 63 297 51 16 14
6 Adenine 125 143 150 65 9 9
7 Adenosine 162 125 61 145 16 16
8 Ajmalicine 156 69 44 119 94 89
9 Allopurinol 215 43 35 163 63 60
10 Betaine 80 86 118 96 117 122
11 Choline 101 61 198 114 35 31
12 Cytidine 5′‐diphosphocholine (CDP‐choline) 172 7 125 179 3 4
13 Cytosine 40 331 15 41 62 64
14 Dehydroascorbic acid (oxidized vitamin C) 19 13 157 34 13 13
15 Diaveridine 90 233 32 56 430 432
16 Dimethylglycine 110 43 176 125 39 36
17 dl‐2‐Aminoadipic acid 34 29 171 23 25 24
18 d‐Mannitol 131 152 142 42 12 10
19 Dopamine 195 8 120 216 15 15
20 d‐Ornithine 117 75 376 90 31 13
21 Edaravone 142 87 80 150 95 81
22 Eicosapentaenoic acid 30 14 7.55 18 389 348
23 Ephedrine 307 20 95 22 17 13
24 gamma‐l‐Glutamyl‐l‐valine 115 98 56 80 129 113
25 Glucosamine 119 175 82 146 28 24
26 Glutathione disulfide 208 84 228 174 29 20
27 Gly‐Val 228 111 43 139 112 36
28 Guanosine 139 199 53 160 28 24
29 Gutathione 76 159 73 146 23 31
30 Harmane 120 98 72 112 82 80
31 His‐Pro 247 28 106 149 46 45
32 Inosine 187 77 51 145 65 63
33 Isopentenyladenosine 342 103 56 175 48 51
34 Isosorbide 121 160 137 32 15 13
35 l‐Abrine 141 58 30 305 53 47
36 l‐Alanine 146 61 154 117 30 29
37 Lanosterol 14 1 213 0.94 0.28 1
38 l‐Arginine 120 110 98 139 132 109
39 l‐Asparagine 162 53 54 143 16 10
40 l‐Aspartate 179 29 67 166 75 58
41 l‐Carnitine 83 87 112 135 51 47
42 l‐Citrulline 317 72 115 35 110 110
43 l‐Glutamine 107 157 67 63 38 30
44 l‐Histidine 334 43 279 168 13 9
45 Linoleic acid 87 104 77 100 127 116
46 Linoleoyl ethanolamide 206 70 151 111 46 53
47 l‐Methionine 443 19 16 99 11 12
48 l‐Tyrosine 215 20 114 232 15 14
49 l‐Valine 46 13 193 26 28 28
50 Maltopentaose 39 315 31 81 25 17
51 Maltotriose 38 301 50 174 19 17
52 Meperidine 76 20 395 30 14 23
53 Miltefosine 95 86 153 185 94 95
54 N,N‐dimethylsphingosine 165 100 156 103 276 276
55 N‐acetyl‐d‐glucosamine 17 534 14 30 15 13
56 N‐acetyl‐l‐tyrosine 95 55 261 165 90 74
57 Nalidixic acid 44 170 15 53 215 215
58 NG,NG‐dimethyl‐l‐arginine (ADMA) 207 81 177 185 39 14
59 Nicotinamide 157 141 75 181 4 4
60 Oleic acid 56 78 88 86 135 146
61 Pantothenate 63 87 288 138 5 5
62 Phenyllactic acid 197 61 112 131 27 25
63 Phytosphingosine 31 94 18 23 689 621
64 p‐Phenylenediamine 95 102 97 99 99 98
65 Pseudouridine 76 204 44 75 46 37
66 Pyridoxine 70 247 32 112 25 21
67 Ribitol 25 190 21 38 184 163
68 Serotonin 174 13 4 266 15 16
69 S‐Methyl‐5′‐thioadenosine 381 151 0.82 54 0.75 0.66
70 Stachyose 40 357 40 73 27 25
71 Tetraethylene glycol 27 36 37 21 13 15
72 Tetramisole 96 210 71 739 46 28
73 Thymine 135 71 96 250 71 71
74 trans‐Vaccenic acid 60 99 90 89 151 150
75 Triethylene glycol 22.10 61 36 32 19 17
76 Trimethoprim 79 302 37 150 36 29
77 Tropine 88 114 51.31 131 224 193
78 Tyramine 211 46 119 138 28 22
79 Uracil 137 133 42 136 22 21
80 Uridine 144 124 45 129 24.02 22.49
81 Xanthosine 447 19 15 45 2.68 2.55
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Abbreviations: GLM, Ganoderma lucidum mushroom; HMM, Hypsizigus marmoreus mushroom; LEM, Lentinus edodes mushroom; PCM, Pleurotus citrinopileatus mushroom; POM, Pleurotus ostreatus mushroom.

3.3. Bacterial diversity and community structure

3.3.1. DNA sequencing

All colon content samples (n = 36) produced 415,315 original raw sequences. A total of 145,965 high‐quality bacterial sequences (average 4054 sequences per sample) were obtained after sequence cleanup (Table 8).

TABLE 8.

OTU classification and classification status identification results statistical table

Group Phylum Class Order Family Genus Species Unclassified Total
C− 7261 7261 7260 5713 2312 202 7 30,016
C+ 2121 2121 2121 2076 1554 36 2 10,031
GLM 4279 4279 4278 3483 1223 111 4 17,657
POM 4699 4699 4698 4029 1519 108 3 19,755
PCM 5333 5333 5333 4419 1754 155 7 22,334
LEM 5477 5477 5477 4579 1867 169 4 23,050
HMM 5510 5510 5510 4609 1844 134 5 23,122
Total 34,680 34,680 34,677 28,908 12,073 915 32 145,965
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Note: “Phylum”, “Class”, “Order”, “Family”, “Genus” and “Species” respectively correspond to the number of OTUs that can be classified into doors, classes, orders, families, genera, and species in each sample, and “Unclassified” refers to the number of OTUs that failed to belong to any known taxon.

3.3.2. OTU classification

Similarity among OTUs that were classified as belonging to different phylum, classes, orders, families, genus, and species (Table 8) based on 16S rRNA gene sequences revealed higher abundances for PCM, LEM, and HMM treatments at phylum and genus levels.

3.3.3. Principle component analysis

Principle component analysis analysis revealed clear divisions among treatment groups. Group B (C+) was “significantly different” from all other treatments, expressing a clear effect of mushroom treatments (p < .05) (Figure 1). More similarity was observed among A (C−), C (GLM), and G (HMM) compared with D (POM), E (PCM), and F (LEM) while similarity between E (PCM) and F (LEM) was higher.

FIGURE 1.

FIGURE 1

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Principle component analysis (PCA) of profiling data from intestinal metabolome. (A, C−) Negative control; (B, C+) positive control; (C, GLM) Ganoderma lucidum mushroom; (D, POM) Pleurotus ostreatus mushroom; (E, PCM) Pleurotus citrinopileatus mushroom; (F, LEM) Lentinus edodes mushroom; (G, HMM) Hypsizigus marmoreus mushroom

3.3.4. Alpha diversity

Table 9 showed that indices of ACE, Chao1, Shannon, and Simpson were significantly affected by PCM and LEM treatments (p < .05), while ACE, Shannon and Simpson indexes were affected by HMM treatment (p < .01). Groups C+ and POM affected the Simpson index. All these four indices were lower in GLM treatment (p < .05).

TABLE 9.

Effect of different dietary mushrooms on alpha diversity of gut microbiota communities in diabetic rats

Treatments ACE Chao 1 Shannon Simpson
C− 1138 ± 321ab 1121 ± 330ab 7.30 ± 0.9a 0.96 ± 0.03a
C+ 465 ± 117b 448 ± 112b 4.60 ± 0.9b 0.78 ± 0.1b
GLM 1018 ± 351ab 993 ± 346ab 6.54 ± 1ab 0.92 ± 0.08a
POM 1092 ± 627ab 1042 ± 591ab 6.59 ± 1ab 0.93 ± 0.05a
PCM 1598 ± 352a 1542 ± 349a 7.75 ± 0.3a 0.97 ± 0.01a
LEM 1303 ± 474a 1249 ± 443a 7.13 ± 0.8a 0.96 ± 0.02a
HMM 1319 ± 485a 1239 ± 452ab 6.82 ± 1a 0.94 ± 0.03a
p‐Value .012 .012 .003 .002
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Note: Values represented means ± standard deviation. Different letters represent a significant difference in Duncan test at p < .05. The first column in the table is groups, and the subsequent columns correspond to the calculation results of the diversity index of Chao1, ACE, Shannon, and Simpson and so on for each sample at the same sequencing depth.

Abbreviations: C−, negative control; C+, positive control; GLM, Ganoderma lucidum mushroom; HMM, Hypsizigus marmoreus mushroom; LEM, Lentinus edodes mushroom; PCM, Pleurotus citrinopileatus mushroom; POM, Pleurotus ostreatus mushroom.

3.3.5. Bacterial community composition (BCC)

At phylum level, C+ treatment showed higher Firmicutes but lower Bacteroidetes abundances. In contrast, HMM treatment showed lower Firmicutes abundance. C+ treatment PCM treatment showed higher Bacteroidetes abundance. LEM treatment showed lower Proteobacteri and Verrucomicrobi abundances. In contrast, HMM treatment showed higher Proteobacteri and Verrucomicrobi abundances (Table 10 and Figure 2).

TABLE 10.

Composition of gut microbiota communities at phylum level (%)

Treatments Firmicutes Bacteroidetes Proteobacteria Verrucomicrobia
C− 62 ± 17ab 23 ± 11ab 9 ± 8a 1 ± 1a
C+ 78 ± 39a 0.69 ± 0.8b 20 ± 39a 0.019 ± 0.02a
GLM 67 ± 19ab 23 ± 22ab 7.31 ± 6a 0.036 ± 0.05a
POM 45 ± 1b 35 ± 20a 14 ± 14a 4.6 ± 10a
PCM 44 ± 6b 42 ± 6a 10 ± 72a 0.011 ± 0.008a
LEM 55 ± 24ab 35 ± 2a 6 ± 5a 0.0035 ± .007a
HMM 36 ± 1b 22 ± 1ab 35 ± 16a 5 ± 11a
p‐Value .05 .006 .169 .577
Open in a new tab

Note: Values represented means ± standard deviation. Different letters represent a significant difference in Duncan test at p < .05.

Abbreviations: C−, negative control; C+, positive control; GLM, Ganoderma lucidum mushroom; HMM, Hypsizigus marmoreus mushroom; LEM, Lentinus edodes mushroom; PCM, Pleurotus citrinopileatus mushroom; POM, Pleurotus ostreatus mushroom.

FIGURE 2.

FIGURE 2

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Microbial populations at phylum level (%). Note: The abscissa depicts the sample name, and the ordinate shows the number of microbial phyla. A: negative control, B: positive control, C: GLM, D: POM, E: PCM, F: LEM, and G: HMM

At genus level, Peptostreptococcaceae abundance was higher in C− and GLM treatments. Enterobacteriaceae abundance was higher in C+, POM, and HMM. Allobaculum abundance was high only in C− treatment (Table 11 and Figure 3).

TABLE 11.

Composition of gut microbiota communities at genus level (%)

Genus Treatments p‐Value
C− C+ GLM POM PCM LEM HMM
Peptostreptococcaceae 0.15 ± 0.2a 0.06 ± 0.101a 0.18 ± 0.2a 0.03 ± 0.04a 0.01 ± 0.001a 0.02 ± 0.03a 0.06 ± 0.09a .296
Enterobacteriaceae 0.01 ± 0.001a 0.2 ± 0.3a 0.07 ± 0.06a 0.07 ± 0.1a 0.01 ± 0.01a 0.01 ± 0.005a 0.15 ± 0.1a .427
Desulfovibrionaceae 0.07 ± 0.07a 0.01 ± 0.001a 0.01 ± 0.001a 0.04 ± 0.05a 0.09 ± 0.07a 0.05 ± 0.05a 0.06 ± 0.05a .117
Ruminococcaceae 0.04 ± 0.03bc 0.01 ± 0.001c 0.03 ± 0.03bc 0.05 ± 0.03bc 0.09 ± 0.03a 0.07 ± 0.05ab 0.03 ± 0.01bc .008
Allobaculum 0.09 ± 0.01a 0.01 ± 0.001a 0.03 ± 0.06a 0.02 ± 0.04a 0.01 ± 0.007a 0.01 ± 0.01a 0.01 ± 0.01a .376
Turicibacter 0.01 ± 0.003a 0.01 ± 0.01a 0.10 ± 0.02a 0.01 ± 0.001a 0.01 ± 0.001a 0.07 ± 0.1a 0.01 ± 0.001a .464
Oscillospira 0.03 ± 0.02abc 0.01 ± 0.001c 0.01 ± 0.003c 0.02 ± 0.03bc 0.04 ± 0.015ab 0.02 ± 0.01bc 0.06 ± 0.03a .005
Lachnospiraceae 0.01 ± 0.01bc 0.01 ± 0.001c 0.01 ± 0.01bc 0.03 ± 0.03abc 0.05 ± 0.03a 0.03 ± 0.003ab 0.02 ± 0.01bc .032
Prevotella 0.01 ± 0.009a 0.01 ± 0.001a 0.03 ± 0.07a 0.01 ± 0.009a 0.04 ± 0.04a 0.02 ± 0.02a 0.01 ± 0.001a .374
Akkermansia 0.01 ± 0.01a 0.01 ± 0.001a 0.01 ± 0.001a 0.05 ± 0.1a 0.01 ± 0.001a 0.01 ± 0.001a 0.05 ± 0.1a .577
Clostridiaceae 0.02 ± 0.02a 0.02 ± 0.027a 0.03 ± 0.02a 0.01 ± 0.002a 0.00 ± 0.001a 0.03 ± 0.06a 0.01 ± 0.01a .664
Ruminococcus 0.01 ± 0.005ab 0.01 ± 0.002b 0.02 ± 0.01a 0.01 ± 0.001ab 0.01 ± 0.003ab 0.01 ± 0.003ab 0.01 ± 0.004ab .219
Bacteroides 0.01 ± 0.003ab 0.01 ± 0.001b 0.01 ± 0.002ab 0.01 ± 0.01ab 0.02 ± 0.02a 0.01 ± 0.008ab 0.01 ± 0.01ab .037
Open in a new tab

Note: Values represented means ± standard deviation. Different letters represent a significant difference in Duncan test at p < .05.

Abbreviations: C−, negative control; C+, positive control; GLM, Ganoderma lucidum mushroom; HMM, Hypsizigus marmoreus mushroom; LEM, Lentinus edodes mushroom; PCM, Pleurotus citrinopileatus mushroom; POM, Pleurotus ostreatus mushroom.

FIGURE 3.

FIGURE 3

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Microbial populations at genus levels (%). Note: The abscissa depicts the sample name, and the ordinate shows the number of microbial genera. A: negative control, B: positive control, C: GLM, D: POM, E: PCM, F: LEM, and G: HMM

4. DISCUSSION

Mushrooms have antihyperglycemic effects on diabetic individuals. LEM and HMM treatments showed lower plasma glucose levels. LEM and PCM changed the ACE, Chao1, Shannon, and Simpson microbial indices significantly. Dietary supplementation of mushrooms reduced plasma glucose level directly due to mushrooms' bioactive compounds (agmatine, sphingosine, pyridoxine, linolenic, and alanine) and indirectly through oligosaccharide (stachyose) and gut microbiota modulation. Thus, LEM and HMM can be used as healthy food ingredients to improve gut microbiome composition in diabetic subjects.

Agmatine shows an antihyperglycemic effect through increasing insulin secretion and glucose uptake in muscles, and preventing hepatic gluconeogenesis via β‐endorphin secretion enhancement (Su et al., 2009). Since agmatine simulates insulin secretion via inhibiting the ATP‐sensitive potassium (KATP) channels in β‐islet cells (Chang et al., 2010; Malaisse et al., 1989; Naoki & Fujiwara, 2019; Nissim et al., 2006, 2014; Shepherd et al., 2012; Su et al., 2008). Such inhibited KATP channel elevates the ATP/ADP ratio, leading to K+ accumulation. This cell depolarization simulates a voltage‐gated Ca2+ channel activity, resulting in Ca2+ influx and consequent insulin secretion (Velasco et al., 2016). In this study, mushroom treatments (LEM and HMM) showed the lowest blood glucose level, which can be explained by the high content of agmatine in LEM and HMM mushrooms. At the intestinal microbiota level, intestinal bacteria altered insulin secretion via converting arginine to agmatine. Such conversion can be catabolized by bacterial acid‐resistant mechanisms, such as Escherichia coli and Enterococcus faecalis (Naoki & Fujiwara, 2019). In our study, LEM treatment showed a high level of Bacteriodetes and Firmicutes phyla, whereas the HMM treatment showed a high level of Proteobacteria. That LEM and HMM mushrooms could manipulate the microbiota composition leading to an increased level of secreted insulin.

Sphingosine (PHS) has a potential therapeutic effect on type II diabetes (Nagasawa et al., 2018). Since PHS activates omega‐3 fatty acid receptor (GPR120) mediating potent insulin‐sensitizing effects. Such activation promotes incretin GLP‐1 secretion, which is notable for having an effects on an anti‐metabolic syndrome (Nagasawa et al., 2018). In this study, the HMM mushroom showed the highest level of PHS bioactive compound along with lower blood glucose levels (Rudd et al., 2020). In addition, the HMM mushroom showed the highest level of sphingosine along with lower blood glucose levels.

Pyridoxine decreases insulin resistance via scavenging the pathogenic reactive carbonyl species (Haus & Thyfault, 2018). Such molecule damages insulin protein via covalent modification of some structural amino acids, as well as, via the formation of adducts with phospholipids and DNA (Haus & Thyfault, 2018). In the current study, the GLM mushroom showed the highest level of pyridoxine bioactive compound along with lower blood glucose levels.

Nutritive acids could have a controversial effect on diabetic individuals. For instance, α‐linolenic acid is a source for the generated oxylipins. Such molecules are lipid mediators affecting type 1 diabetes (Buckner et al., 2021). In our study, the LEM and HMM mushrooms showed the highest level of linolenic acid along with lower blood glucose levels. However, alanine may induce hyperglycemia in diabetic individuals. Since alanine aminotransferases increased levels are marked in diabetes hepatic cells (Okun et al., 2021). In the current study, the LEM mushroom showed the highest level of alanine along with lower blood glucose levels. The amino acids and fatty acids classes should be considered in the research of diabetic individuals' diet.

In literature, the mushrooms showed different polysaccharides and their effect on diabetes, for example, G. lucidum (polyheterosaccharides); P. ostreatus, (polyheterosaccharides); P. citrinopileatus (acid polysaccharide); L. edodes (glucan, heteropolysaccharides); and H. marmoreus (glycoprotein). Chlorella pyrenoidosa polysaccharides with a low molecular weight (>3000 Da) showed an hypolipidemic effect in rat (Agunloye & Oboh, 2022; Hossain et al., 2021; Jayasuriya et al., 2015; Qiu et al., 2022). Chisandra sphenanthera polysaccharide (191.18 kD) showed antidiabetic effect in rats with type 2 diabetes (Niu et al., 2017). Dendrobium officinale leaf polysaccharides of different molecular weights were orally administered daily at 200 mg/kg/day, this level alleviated type II diabetes in an adult mouse (Fang et al., 2022). Thus, polysaccharides have an effect on diabetes whether they are low or high molecular weights. Stachyose is a non‐reducing tetrasaccharide molecule which decreases the blood glucose level in alloxan‐induced diabetic rats (Zhang et al., 2004). In addition, stachyose adjusts blood lipid levels in diabetic individuals (Chen et al., 2019). In the current study, the GLM mushroom showed the highest level of stachyose bioactive compound along with lower blood glucose levels. At the intestinal microbiota level, stachyose as a functional oligosaccharide regulates the intestinal microflora balance. Such prebiotic shifts of gut microbiota including Bifidobacterium and Lactobacillus as they are two common genera affecting a host health (Liu, Jia, et al., 2018; Liu, Wang, et al., 2018). In this study, the LEM and HMM mushrooms showed the highest level of Lactobacillus genus along with lower blood glucose levels. LEM and HMM mushrooms could modulate blood glucose levels and intestinal microbiota in diabetic individuals.

Changes in the gut microbiota composition are associated with multiple chronic disease pathologies, such as type 2 diabetes mellitus (Tang et al., 2017). Dietary fiber intake protects against diabetes by lowering dietary glycemic (Anderson et al., 2009). For example, oyster and button mushrooms have hypoglycemic effects, which reduce the fasting blood glucose level (Shehata et al., 2010). G. lucidum extract reduces blood glucose and insulin levels in rats (Hikino et al., 1985). Dietary supplements of I. bartlettii, Bifidobacterium longum, and B. cellulosilyticus in combination with water‐soluble viscous fibers improve glucose homeostasis and dyslipidemia. Since gut microbiota affects insulin resistance by decreasing TNF‐ α level in plasma and improving fasting blood glucose level in mice fed a high‐fat diet (Chuang et al., 2012).

Mushroom intake increases lactic acid‐producing bacteria (Lactobacillus, Lactococcus and Streptococcus) and SCFA‐producing bacteria (Allobaculum, Bifidobacterium and Ruminococcus), which can be explained by the mushroom's fiber content (Takamitsu et al., 2018). Butyrate‐producing R. inulinivorans abundance is higher in healthy individuals than in T2D individuals (Tang et al., 2017). F. prausnitzii abundance is low in individuals with T2D (Karlsson et al., 2013; Qin et al., 2012). Insulin resistance is related to B. wadsworthia and C. bolteae abundances (Qin et al., 2012). The species A. muciniphila, B. faecis, B. nordii, B. cellulosilyticus, B. pectinophilus, I. bartlettii, O. splanchnicus, D. longicatena, and R. inulinivorans were negatively associated with insulin resistance or dyslipidemia (Brahe et al., 2015). Bifidobacterium (B. longum) abundance was higher in healthy individuals than in obese individuals and T2D (Karlsson et al., 2013). The more decrease in butyric acid production, the more decrease in C. leptum abundance (Wang et al., 2015). Butyrate has an anti‐inflammatory activity that could improve insulin resistance (Brahe et al., 2013). F. prausnitzii affects insulin sensitivity, which may be due to its ability to produce butyrate (Louis & Flint, 2009). In this study, Allobaculum was increased in positive control treatment, whereas the Ruminococcus was increased in GLM treatment.

Regarding the mushroom mix potence, as individual mushrooms showed a significant effect on blood glucose level that mushroom mix could provide more effective role in blood glucose control regarding the gathered bioactive compounds and their possible compatible roles. Future studies are required to investigate the potency of mushroom mix with more diabetes parameters to reveal the underlying mechanism on diabetes‐based long‐term treatment.

5. CONCLUSIONS

Dietary supplementation with mushrooms reduced the plasma glucose level and modulated gut microbiota in diabetic rats. Mushrooms showed a direct antihyperglycemic effect due to their content of agmatine, stachyose, phytosphingosine, and pyridoxine bioactive compounds. Mixed dietary mushrooms could develop a food ingredient with an effective and specific health functionality for diabetic individuals.

CONFLICT OF INTEREST

None.

ACKNOWLEDGMENT

This research was funded by the Development of Standard Models for Domestic Animal and Poultry Production of China (2017YFD0502001).

Hussein, A. , Ghonimy, A. , Jiang, H. , Qin, G. , El‐Ashram, S. , Hussein, S. , Abd El‐Razek, I. , El‐Afifi, T. , & Farouk, M. H. (2023). LC/MS analysis of mushrooms provided new insights into dietary management of diabetes mellitus in rats. Food Science & Nutrition, 11, 2321–2335. 10.1002/fsn3.3236

Contributor Information

Hailong Jiang, Email: hljiang@jlau.edu.cn.

Mohammed Hamdy Farouk, Email: mhfarouk@azhar.edu.eg, Email: mhfarouk@jlau.edu.cn.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author.

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Associated Data

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Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

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